Instant Personalized Large Language Model Adaptation via Hypernetwork
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866908600634441728 |
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| author | Tan, Zhaoxuan Zhang, Zixuan Wen, Haoyang Li, Zheng Zhang, Rongzhi Chen, Pei Mo, Fengran Liu, Zheyuan Zeng, Qingkai Yin, Qingyu Jiang, Meng |
| author_facet | Tan, Zhaoxuan Zhang, Zixuan Wen, Haoyang Li, Zheng Zhang, Rongzhi Chen, Pei Mo, Fengran Liu, Zheyuan Zeng, Qingkai Yin, Qingyu Jiang, Meng |
| contents | Personalized large language models (LLMs) tailor content to individual preferences using user profiles or histories. However, existing parameter-efficient fine-tuning (PEFT) methods, such as the ``One-PEFT-Per-User'' (OPPU) paradigm, require training a separate adapter for each user, making them computationally expensive and impractical for real-time updates. We introduce Profile-to-PEFT, a scalable framework that employs a hypernetwork, trained end-to-end, to map a user's encoded profile directly to a full set of adapter parameters (e.g., LoRA), eliminating per-user training at deployment. This design enables instant adaptation, generalization to unseen users, and privacy-preserving local deployment. Experimental results demonstrate that our method outperforms both prompt-based personalization and OPPU while using substantially fewer computational resources at deployment. The framework exhibits strong generalization to out-of-distribution users and maintains robustness across varying user activity levels and different embedding backbones. The proposed Profile-to-PEFT framework enables efficient, scalable, and adaptive LLM personalization suitable for large-scale applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_16282 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Instant Personalized Large Language Model Adaptation via Hypernetwork Tan, Zhaoxuan Zhang, Zixuan Wen, Haoyang Li, Zheng Zhang, Rongzhi Chen, Pei Mo, Fengran Liu, Zheyuan Zeng, Qingkai Yin, Qingyu Jiang, Meng Computation and Language Personalized large language models (LLMs) tailor content to individual preferences using user profiles or histories. However, existing parameter-efficient fine-tuning (PEFT) methods, such as the ``One-PEFT-Per-User'' (OPPU) paradigm, require training a separate adapter for each user, making them computationally expensive and impractical for real-time updates. We introduce Profile-to-PEFT, a scalable framework that employs a hypernetwork, trained end-to-end, to map a user's encoded profile directly to a full set of adapter parameters (e.g., LoRA), eliminating per-user training at deployment. This design enables instant adaptation, generalization to unseen users, and privacy-preserving local deployment. Experimental results demonstrate that our method outperforms both prompt-based personalization and OPPU while using substantially fewer computational resources at deployment. The framework exhibits strong generalization to out-of-distribution users and maintains robustness across varying user activity levels and different embedding backbones. The proposed Profile-to-PEFT framework enables efficient, scalable, and adaptive LLM personalization suitable for large-scale applications. |
| title | Instant Personalized Large Language Model Adaptation via Hypernetwork |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2510.16282 |